code: def correct_action(self, observation, to_simulate_action, sub_id):
      if sub_id != -1:
          if observation.time_before_cooldown_sub[sub_id] != 0:
              legal_action_vec = deepcopy(
                  self.do_nothing_action.to_vect()
              )
              return self.do_nothing_action
          else:
              legal_action_vec = deepcopy(to_simulate_action.to_vect())
  
          sub_topo = self.sub_topo_dict[sub_id]
          if np.any(sub_topo == -1):  # line disconnected
              start, end = self.action_to_sub_topo[sub_id]
              action_topo = legal_action_vec[start:end].astype(
                  "int"
              )  # reference
              action_topo[
                  np.where(sub_topo == -1)[0]
              ] = 0  # done't change bus=-1
              legal_action_vec[start:end] = action_topo
          legal_action = self.action_space.from_vect(legal_action_vec)
  
      elif sub_id == -1:
          legal_action = to_simulate_action
      else:  # TODO remove
          legal_action = self.do_nothing_action
      return legal_action
  
file_path: PARL/benchmark/torch/NeurIPS2020-Learning-to-Run-a-Power-Network-Challenge/track2/rl_agent.py
imports: import os
  import numpy
  from copy import deepcopy
  from utils import process
  from powernet_model import PowerNetModel
  from grid2op.Agent import BaseAgent
  import torch
label_desc: Use image node_id 8 for calling the RLAgent obj's underlying member method code with example usage: obj.correct_action(observation, to_simulate_action, sub_id) and returns: legal_action, self